PhD Student in AI and Cybersecurity

About the role

Four-year fully funded PhD at Jönköping University’s School of Engineering, in the AI and Cybersecurity research group. The project develops machine learning methods for intrusion detection, adversarial robustness, or threat intelligence. Deadline 1 June 2026.

Responsibilities

• Design ML models for cybersecurity tasks (anomaly detection, malware classification, network threat analysis).
• Stress-test models against adversarial attacks and develop hardening techniques.
• Publish in IEEE Security and Privacy, USENIX Security, ACSAC, and ML venues.
• Co-supervise Master’s students and teach assistant on one course per year.

Requirements

• Master’s degree in computer science, information security, or a closely related field.
• Solid grounding in machine learning and at least one cybersecurity sub-area.
• Python proficiency; familiarity with PyTorch or TensorFlow.
• Fluent English. Swedish is welcomed but not required.

Nice to have

• Prior research or industry experience in security.
• Familiarity with adversarial ML, federated learning, or differential privacy.
• A first-author publication or open-source security tool contribution.

How to apply

Open the apply link and submit CV, transcripts, motivation letter, Master’s thesis, and two reference contacts via Jönköping University’s recruitment system before 1 June 2026.